Thursday, December 11, 2014

I'm doing an analysis of Diurnal Temperature Range (DTR; more on that when published) but as part of this I just played with a little toy box model and the result is sufficiently of general interest to highlight here and maybe get some feedback.

So, for most stations in the databank we have data for maximum (Tx) and minimum (Tn) that we then average to get Tm. Now, that is not the only transform possible - there is also DTR which is Tx-Tn. Although that is not part of the databank archive its a trivial transform. In looking at results running NCDC's pairwise algorithm distinct differences in breakpoint detection efficacy and adjustment distribution arise, which have caused great author team angst.

This morning I constructed a simple toy box where I just played what if. More precisely what if I allowed seeded breaks in Tx and Tn in the bound -5 to 5 and considered the break size effects in Tx, Tn, Tm and DTR:

The top two panels are hopefully pretty self explanatory. Tm and DTR effects are orthogonal which makes sense. In the lowest panel (note colours chosen from colorbrewer but please advise if issues for colour-blind folks):
red: Break largest in Tx
blue: Break largest in Tn
purple: break largest in DTR
green: break largest in Tm (yes, there is precisely no green)
Cases with breaks equal in size are no colour (infintesimally small lines along diagonal and vertices at Tx and Tn =0)

So …

if we just randomly seeded Tx and Tn breaks in an entirely uncorrelated manner into the series then we would get 50% of breaks largest in DTR and 25% each in Tx and Tn. DTR should be broader in its overall distribution and Tm narrower with Tx and Tn intermediate.

if we put in correlated Tx and Tn breaks such that they were always same sign (but not magnitude) then they would always be largest in either Tx or Tn (or equal with Tm when Tx=Tn)

If we put in anti-correlated breaks then they would always be largest in DTR.

Perhaps most importantly, as alluded to above, breaks will only be equal largest for Tm in a very special set of cases where Tx break = Tn break. Breaks, on average will be smallest in Tm. If breakpoint detection and adjustment is a signal to noise problem its not sensible to look where the signal is smallest. This has potentially serious implications for our ability to detect and adjust for breakpoints if we limit ourselves to Tm and is why we should try to rescue Tx and Tn data for the large amount of early data for which we only have Tm in the archives.

Maybe in future we can consider this as an explicitly joint estimation problem of finding breaks in the two primary elements and two derived elements and then constructing physically consistent adjustment estimates from the element-wise CDFs. Okay, I'm losing you now I know so I'll shut up ... for now ...

Tuesday, December 9, 2014

It has been nearly six months since we have released the first version of the databank. While this was a big achievement for the International Surface Temperature Initiative, our work is not done. We have taken on many different tasks since the release, and a brief description is below:

Monthly Update System
As described in this post, we have implemented a monthly update system appending near real time (NRT) data into the databank. On the 5th of each month 4 sources (ghcnd, climat-ncdc, climat-uk, mcdw-unpublished) update their Stage 1 data, and on the 11th, their common formatted data (Stage 2) are then updated. In addition, an algorithm is applied appending new data to the recommended merge, and that is updated on the 11th as well.

Bug Fixes
Users have submitted some minor issues with version 1. Some stations in Serbia were given a country code of "RB" when they should have been given "RI." These have been addressed, and a new version of the databank (v1.0.1) was released.

There have been concerns about how the station name is displayed. Non-ASCII characters pose problems with some text interpreters. A module has been created in the Stage 1 to Stage 2 conversion scripts where these characters are either changed or removed to avoid this problem in the future.

Of course issues could still exist, if you find any please let us know! As an open and transparent initiative, we encourage constructive criticism and will apply any reasonable suggestions to future versions.

New Sources
We have acquired new sources that will be added as Stage 1 and Stage 2 data soon, including

300 UK Stations from the Met Office

German data released by DWD

EPA's Oregon Crest to Coast Dataset

LCA&D: Latin American Climate Assessment and Dataset

Daily Chinese Data

NCAR Surface Libraries

Stations from Meteomet project

Libya Stations sent by their NMS

C3/EURO4M Stations

Additional Digitized Stations from the University of Giessen

Homogenized Iranian Data

It is not too late to submit new data. If you have a lead on sources please let us know at data.submission@surfacetemperatures.org. We will freeze the sources again on February 28th, 2015, in order to work on the next version of the merge.

Friday, December 5, 2014

NOAA's National Climatic Data Center have undertaken an inventory of their substantial basement holdings of hard copy data. These include a rich mix of data types on varied media including paper, fiche and microfilm.

One row of several dozen in the NCDC archive of hard copy paper holdings from around the world

Microfilm holdings arising from Europe over the second world war

Some, but far from all, of this data has been imaged and / or digitized. NCDC have now released the catalogue online and made it searchable. The catalogue interface can be found at https://www.ncdc.noaa.gov/cdo/f?p=222 (click on search records). The degree to which a given holding has been catalogued varies but this is a good place to at least begin to ascertain what holdings there are there and what their status is. For example searching on American Samoa as country provides a list of holdings most of which are hard copy only.

Example search results for American Samoa

For those interested in aspects of data rescue, this is likely to be a useful tool to ascertain whether NCDC hold any relevant records. By reasonable estimates at least as much data exists in hard copy / imaged format as has been digitised for the pre-1950 period. That is a lot of unknown knowns and could provide such rich information to improve understanding ...

Wednesday, November 26, 2014

We have produced a set of one-sider flyers to promote the initiative and its aims and to try to engender additional inputs, collaborations and contributions. These will be taken by Kate Willett to the forthcoming COP meeting in Peru next month.

We strongly encourage use of these flyers at appropriate venues to support the further advancement of our work.

Our more eagle eyed readers would have noted above a new strand to our work. I am delighted to say that we have, following the most recent steering committee call, formally recognized the efforts led by Victor Venema and Renate Auchmann to populate and exploit a database of parallel measurements by instigating a new expert team under the databank working group. We shall do all we can to support this important effort and in the first instanace we encourage readers to help us in the identification and collection of such holdings.

Wednesday, November 5, 2014

The ISTI benchmark working group includes a PhD student looking at benchmarking daily temperature homogenisation algorithms. This largely follows the concepts laid out in the benchmark working group's publication. Significant progress has been made in this field. This post announces the release of a small daily benchmark
dataset focusing on four regions in North America. These regions can be
seen in Figure 1.

Figure
1 Station locations of the four benchmark regions. Blue stations are in
all worlds. Red stations only appear in worlds 2 and 3.

These benchmarks
have similar aims to the global benchmarks that are currently being
produced by the ISTI working group, namely to:

Assess the performance of current homogenisation algorithms and provide feedback to allow for their improvement

Assess how realistic the created benchmarks are, to allow for improvements in future iterations

Quantify the
uncertainty that is present in data due to inhomogeneities both before
and after homogenisation algorithms have been run on them

A perfect
algorithm would return the inhomogeneous data to their clean form –
correctly identifying the size and location of the inhomogeneities and
adjusting the series accordingly. The inhomogeneities that have been
added will not be made known to the testers until the completion of the
assessment cycle – mid 2015. This is to ensure that the study is as fair
as possible with no testers having prior knowledge of the added
inhomogeneities.

The data are formed into three worlds, each consisting of the
four regions shown in Figure 1. World 1 is the smallest and contains
only those stations shown in blue in Figure 1, Worlds 2 and 3 are the
same size as each other and contain all the stations shown.

Homogenisers are requested to prioritise running their
algorithms on a single region across worlds instead of on all regions in
a single world. This will hopefully maximise the usefulness of this
study in assessing the strengths and weaknesses of the process. The
order of prioritisation for the regions is Wyoming, South East, North
East and finally the South West.

This study will be more effective the more participants it has
and if you are interested in participating please contact Rachel Warren
(rw307 AT exeter.ac.uk). The results will form part of a PhD thesis and
therefore it is requested that they are returned no later than Friday
12th December 2014. However, interested parties who are unable to meet
this deadline are also encouraged to contact Rachel.

There will be a further smaller release in the next week that is
just focussed on Wyoming and will explore climate characteristics of
data instead of just focusing on inhomogeneity characteristics.

Benchmarking,
in this context, is the assessment of homogenisation algorithm
performance against a set of realistic synthetic worlds of station data
where the locations and size/shape of inhomogeneities are known a priori.
Crucially, these inhomogeneities are not known to those performing the
homogenisation, only those performing the assessment. Assessment of both
the ability of algorithms to find changepoints and accurately return
the synthetic data to its clean form (prior to addition of
inhomogeneity) has three main purposes:

1) quantification of uncertainty remaining in the data due to inhomogeneity 2) inter-comparison of climate data products in terms of fitness for a specified purpose 3) providing a tool for further improvement in homogenisation algorithms

Here
we describe what we believe would be a good approach to a comprehensive
homogenisation algorithm benchmarking system. Thfis includes an
overarching cycle of: benchmark development; release of formal
benchmarks; assessment of homogenised benchmarks and an overview of
where we can improve for next time around (Figure 1).

Creation of realistic clean synthetic station data

Firstly,
we must be able to synthetically recreate the 30000+ ISTI stations such
that they have the correct variability, auto-correlation and
interstation cross-correlations as the real data but are free from
systematic error. In other words, they must contain a realistic seasonal
cycle and features of natural variability (e.g., ENSO, volcanic
eruptions etc.). There must be a realistic persistence month-to-month in
each station and geographically across nearby stations.

Creation of realistic error models to add to the clean station data

The
added inhomogeneities should cover all known types of inhomogeneity in
terms of their frequency, magnitude and seasonal behaviour. For example,
inhomogeneities could be any or a combination of the following:
- geographically or temporally clustered due to events which affect
entire networks or regions (e.g. change in observation time); - close to end points of time series; - gradual or sudden; - variance-altering; - combined with the presence of a long-term background trend; - small or large; - frequent; - seasonally or diurnally varying.

Design of an assessment system

Assessment
of the homogenised benchmarks should be designed with the three
purposes of benchmarking in mind. Both the ability to correctly locate
changepoints and to adjust the data back to its homogeneous state are
important. It can be split into four different levels: -
Level 1: The ability of the algorithm to restore an inhomogeneous world
to its clean world state in terms of climatology, variance and trends. - Level 2: The ability of the algorithm to accurately locate changepoints and detect their size/shape. -
Level 3: The strengths and weaknesses of an algorithm against specific
types of inhomogeneity and observing system issues. -
Level 4: A comparison of the benchmarks with the real world in terms of
detected inhomogeneity both to measure algorithm performance in the
real world and to enable future improvement to the benchmarks.

The benchmark cycle

This
should all take place within a well laid out framework to encourage
people to take part and make the results as useful as possible. Timing
is important. Too long a cycle will mean that the benchmarks become
outdated. Too short a cycle will reduce the number of groups able to
participate.

Producing
the clean synthetic station data on the global scale is a complicated
task that has now taken several years but we are close to completion of a
version 1. We have collected together a list of known
regionwide inhomogeneities and a comprehensive understanding of the many
many different types of inhomogeneities that can affect station data.
We have also considered a number of assessment options and decided to
focus on levels 1 and 2 for assessment within the benchmark cycle. Our
benchmarking working group is aiming for release of the first benchmarks
by January 2015.

Friday, September 12, 2014

Since the official release back in June, we have worked to keep the databank updated with the most recent data. Each month we will post new data from sources that update in near-real-time (NRT), along with an updated version of the recommended merge with the latest data appended. Stage 1 data (digitized in its original form) will be updated no later than the 5th of each month, and then Stage 2 (common formatted data) and Stage 3 (merged record) data will be updated no later than the 11th of the month.

So what data gets updated in our NRT system? We have determined four sources that have updated data within the first few days of the month. They are the CLIMAT streams from NCDC as well as the UK, the unpublished form of the monthly climatic data for the world (MCDW) and finally GHCN-D. Similar to the merge program, a hierarchy is placed determining which source its data appends to if there are conflicts. The hierarchy is here:

1) GHCN-D
2) CLIMAT-UK
3) CLIMAT-NCDC
4) MCDW-Unpublished

An overview of the system is shown here in this flow diagram (Click on image to enlarge):

The algorithm to append data looks for station matches through the same metadata tests as described in the merge program. These include geographic distance, height distance, and station name similarity using the Jaccard Index. If the metadata metric is good, then an ID test is used to determine station match. Because the four input sources have either a GHCN-D or WMO ID, the matching is much easier here than in the merge program. Once a station match is found, new data from the past few months are appended. Throughout this process, no new stations are added.

We have had two monthly updates so far. As always the latest recommended merge data can be found on our ftp page here, along with older data placed in the archive here. Note that we are only updating the recommended merge, and not the variants. In addition, the merge metadata is not updated, because no new merge has been applied yet. We plan to have another merge out sometime in early 2015.

I’ve recently modified ccc-gistemp so that it can use the dataset recently released by the International Surface Temperature Initiative. Normally ccc-gistemp uses GHCN-M, but the ISTI dataset is much larger. Since ISTI publish the Stage 3 dataset in the same format as GHCN-M v3 the required changes were relatively minor, and Climate Code Foundation appreciates the fact that ISTI is published in several formats, including GHCN-M v3.

The ISTI dataset is not quality controlled, so, after re-reading section 3.3 of Lawrimore et al 2011, I implemented an extremely simple quality control scheme, MADQC. In MADQC a data value is rejected if its distance from the median (for its station’s named month) exceeds 5 times the median absolute deviation (MAD, hence MADQC); any series with fewer than 20 values (for each named month) is rejected.

So far I’ve found MADQC to be reasonable at rejecting the grossest non climatic errors.

Let’s compare the ccc-gistemp analysis using the ISTI Stage 3 dataset versus using the GHCN-M QCU dataset. The analysis for each hemisphere:

Now we can see the agreement in the northern hemisphere is excellent. In the southern hemisphere agreement is very good. The trend is slightly higher for the ISTI dataset.

The additional data that ISTI has gathered is most welcome, and this analysis shows that the warming trend in both hemispheres was not due to choosing a particular set of stations for GHCN-M. The much more comprehensive station network of ISTI shows the same trends.

Thursday, July 24, 2014

The World Meteorological Organization’s Commission
for Climatology had its four-yearly meeting in Heidelberg, Germany, from 3-8
July, preceded by a Technical Conference from 30 June – 2 July. The Commission
is the central body for climate-related activities in WMO, and has a major role
in establishing international standards and setting international work programs
in the climate field, particularly through setting up networks of Expert Teams
and Task Teams to work on particular issues. ItsPresident (re-elected at the meeting) is Tom
Peterson of NCDC, who will be well-known to many of you. The International
Surface Temperature Initiative was set up as the result of a resolution of the
last Commission for Climatology meeting, in 2010.

I made a presentation to the Technical Conference
on the current status of ISTI. By happy coincidence, this presentation was
scheduled for the morning on 1 July, a few hours after the release of the first
version of the ISTI databank. The presentation appeared to be well-received;
there were few direct questions or follow-ups, but the pile of leaflets we
brought describing ISTI (once they got there, after a couple of bonus days
enjoying Berlin with the rest of my luggage) was a lot smaller at the end of
the week than it was at the start. One particular reason for targeting the
Commission audience is that many of the attendees at Commission meetings are
senior managers in their national meteorological services (often the head of
the climate division, or equivalent), and so potentially have more influence
over decisions to make data available to projects such as ISTI than individual
scientists would.

Slow progress is also being made in two other areas
of WMO of interest to ISTI. The inclusion of at least some historic climate data
amongst the set of products which countries agree to freely exchange has been a
long-standing goal of ours. The key decisions on this will be made at the full
WMO Congress, which will be held next year, but progress to date (including
through the recent WMO Executive Council meeting) is encouraging. There are
also moves to include the month’s daily data in monthly CLIMAT messages, which
are the principal means of exchanging current climate data through the WMO
system but currently only contain monthly data. This will be very useful for
the ongoing updating of data sets, as it will make daily data available which
can be assumed to be for a full 24-hour day and is likely to have received at
least some quality control (neither of which is necessarily true for the
real-time synoptic reports which are the primary current source of recent daily
and sub-daily data). Considerable technical work remains to be done, though, to
implement this, even once it is formally endorsed.

Data rescue and climate database systems continue
to be a high priority of the Commission, with several initiatives outlined at
the meeting. Among them are proposals for an international data rescue portal,
which (among other things) would potentially facilitate crowd-sourced
digitisation. It is, however, an indication of how much work still remains to
be done in many parts of the world that, according to results of a survey
reported at the meeting, 25% of responding countries still stored their
country’s climate data in spreadsheets or flat files, and 40% had a climate
database system which was not fully functioning or not functioning at all.

The Commission also agreed to establish a new Task
Team on Homogenisation. The full membership (and chairing) of this group are
not yet clear but I will almost certainly be part of it. This team will be
working closely with ISTI, but will also have a major focus on supporting the
implementation of homogenised data sets which contribute to operational data
products nationally and internationally.

Also of interest to ISTI is a new WMO initiative to
formally recognise “centennial stations”, which, as the name implies, are
stations which have existed with few or no changes for 100 years or more.
Countries are to be asked to identify such stations, whose data will clearly be
of considerable value to ISTI, if not already part of our databank. Free access
to data and relevant metadata are among the recommendations for centennial
stations.

And one advantage of holding an international
meeting during the World Cup: it provides an instant conversation-starter with
delegates of almost any country. (Perhaps fortunately for the Brazilian
delegation, the meeting finished just before the semi-finals).

(Update 5 August: the resolution which came out of the WMO Executive Council meeting is available at

We hope to have a meeting report out within a matter of days to weeks. We will post this here.

Overall there was a lot of active participation and many new directions to be taken in the analysis of surface temperatures. Our thanks go out to both SAMSI and IMAGe for facilitating this meeting and to all the participants for being active. More details to appear soon ...

Monday, June 30, 2014

The International Surface Temperature Initiative is pleased to release version 1 of a new monthly dataset that brings together new
and existing sources of surface air temperature. Users are provided a way to more
completely track the origin of surface air temperature data from its earliest
available source through its integration into a merged data holding. The data
are provided in various stages that lead to the integrated product. This release is the culmination of three years effort by an
international group of scientists to produce a truly comprehensive, open and
transparent set of fundamental monthly data holdings. The databank has been previously available in beta form, giving the public a chance to provide feedback. We have received numerous comments and have updated many of our sources. This release consists of:

Over 50 distinct sources, submitted to the databank to date in Stage 0 (hardcopy / image; where
available), Stage 1 (native digital format), and Stage 2 (converted to common
format and with provenance flags).

All code to convert the Stage 1 holdings to Stage 2.

A recommended merged product and several
variants which have all been built off the Stage 2 holdings. 2 ASCII formats are provided (ISTI format, GHCN format), along with a CF Compliant netCDF format.

All code used to process the data merge, along with statistical auxiliary files.

Documentation necessary to understand at a high
level the processing of the data, including the location of the manuscript published in Geoscience Data Journal.

The entire databank can be found here and the merged product is located here. Earlier betas are also found here. Because the databank is version controlled, we welcome any feedback. We will be providing updates on the blog regarding any new releases.

Saturday, June 28, 2014

Since records of surface temperature started being made
there have been iterations of the fixed points standards used by
national metrological institutes (that is not a typo). Assuming that all
meteorological measurements through time have been made to such
standards (which may be a considerable stretch) this would have imparted
changes to the records that are not physical in origin. As part of meteomet
efforts have been made to understand this. It is a relatively small
effect compared to effects of other long recognized data issues. Nevertheless it is important to properly and systematically consider all
sources of potential biases as exhaustively as possible.

Temperature
is one of the main quantities measured in meteorology and plays a key
role in weather forecasts and climate determination. The instrumental
temperature recordings now spans well over a century, with some records
extending back to the 17th century, and represents an invaluable tool in
evaluating historic climatic trends. However, ensuring the quality of
the data records is challenging, with issues arising from the wide range
of sensors used, how the sensors were calibrated, and how the data was
recorded and written down. In particular, the very definition of the
temperature scales have evolved. While they have always been based on
calibration of instruments via a series of material phase transitions
(fixed points), the evolution of sensors, measuring techniques and
revisions of the fixed points used has introduced differences that may
lead to difficulties when studying historic temperature records. The
conversion program here presented deals with this issue for 20th century
data by implementing a proposed mathematical model to allow the
conversion from historical scales to the currently adopted International
Temperature Scale of 1990 (ITS-90). This program can convert large
files of historical records to the current international temperature
scale, a feature which is intended to help in the harmonisation
processes of long historic series. This work is part of the project
“MeteoMet” funded by the EURAMET, the European association of National
Institutes of Metrology, and is part of a major general effort in
identifying the several sources of uncertainty in climate and
meteorological records.

Michael de Podesta, who has served on the steering committee since ISTI's inception, reviewed the software for ISTI and had the following summary:

Assuming
that calibration procedures immediately spread throughout the world –
homogenisation algorithms might conceivably see adjustments in 1968,
with smaller adjustments in 1990.If
undetected, the effect would be to create a bias in the temperature
record. This is difficult to calculate since the bias is temperature
dependent, but if the mean land-surface temperature is ~10°C and if
temperature excursions are typically ±10 °C then one might expect that
the effect to be that records prior to 1968 were systematically
overestimated by about 0.005 °C, and records between 1968 and 1990 by
about 0.003 °C.

Michael's full summary which includes some graphical and tabular summaries can be found here.

The code package is a windows operating system based package. It is available here.

Wednesday, June 4, 2014

Just briefly to note that a discussion paper is now open for comment authored by the members of the benchmarking working group. This paper discusses the concepts and frameworks that will underpin all aspects of the benchmarking and assessment exercise. Its open to review until July 30th. Please do, if you have time and inclination, pop along and have a read and provide a constructive (!) review. The discussion site is at http://www.geosci-instrum-method-data-syst-discuss.net/4/235/2014/gid-4-235-2014.html .

Also, watch this space at the end of this month for exciting developments on the first pillar of the ISTI framework - the databank.

Finally, we are rapidly hurtling towards the SAMSI/IMAGe/ISTI workshop on surface temperatures and their analyses. Its going to be a busy few weeks so expect this blog to be somewhat less moribund than of late ...

Friday, April 4, 2014

The EarthTemp
Network aims to stimulate new international collaboration in measuring and
understanding the surface temperatures of Earth. Motivated by the need for
better understanding of how different types of measurements relate, including
both in situ and satellite observations the network is international but funded
by the UK’s Natural Environment Research
Council.

The 2014 meeting will bring together about
60 researchers from all over the world who specialise in different types of
measurement of surface temperature. The meeting will be specifically designed
to review the latest science of surface temperatures for Africa, identify
future developments, and, importantly facilitate new connections and
collaborations between researchers who may not normally meet. Therefore, the
programme emphasizes activities that actively increase networking and
interaction between the participants and that enable structured discussions
towards the goal of identifying key research opportunities. A preliminary
programme can be found at the EarthTemp
Network website, follow the links to the ‘Annual themes and workshop’ link
on the left hand side.

The meeting will be held on the 23-25th
June 2014 at The Karlsruhe Institute of Technology, Karlsruhe, Germany. Registration
is free and lunch will be provided each day of the meeting, with a dinner on
the Tuesday evening also included. If you are interested in attending this
meeting please go to the to the EarthTemp webpage and follow links to the
Annual themes and workshop. As places are limited we
strongly encourage you to act quickly.

The workshop will be immediately followed by the GlobTemperature 2nd
user consultation meeting in the same location, on the 25-26th June.
GlobTemperature is a European Space Agency funded project to support the land
surface temperature (LST) community to develop products and access mechanisms
better suited to users. Participants at the EarthTemp workshop are very welcome
to attend this meeting. More details can be found on the GlobTemperature website.

Thursday, February 6, 2014

We have long recognized that the enabling framework aspects of the Initiative (databank, benchmarks, data serving) are but one aspect of the problem. What is needed in addition are new approaches to the data homogenization so that we can better understand the data and their uncertainties. This is not something the Initiative can mandate nor something that the 10 cents coin I found following exhaustive searching down the back of my sofa will get us very far in funding. So, we have been and continue to pursue novel means to increase the number of independent groups and individuals undertaking the analysis of the data.

As one such activity, the Initiative put forward a proposal for a SAMSI (Statistical and Applied Mathematical Sciences Institute) summer program activity - which got selected. Over the past few months we have been working with colleagues from SAMSI and NCAR IMAGe (Institute for Mathematics Applied to Geosciences) who joined as substantive co-sponsors to arrange the meeting logistics. We are now in a position to announce the workshop.

So, without further ado ...

Applications are invited for participation in a workshop to be held in Boulder, Colorado July 8th-16th. The aim of the workshop is to develop new and novel techniques for the homogenisation of land surface air temperature data holdings. The workshop participants will have access to the almost 32,000 stations held in the first version databank release (which will be publicly available by then) and also to several of the benchmark datasets. The workshop will mainly be practically based - with few talks and lots of coding and discussions either in plenary or in smaller breakout groups. A final agenda will be forthcoming nearer the time.

Applications are welcome from all. The final meeting is space limited to 44 people. Participants from non-traditional backgrounds, early career scientists and members of under-represented groups are particularly encouraged to apply.